Bighorn data processing

library(tidyverse)

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library(dplyr)
library(lubridate)

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library(plotly)

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library(ggplot2)
library(here)

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Loading in the data

# read in met data
met <- read_csv(here('bighorn_met_composite.csv')) %>%
  mutate(dt_use = parse_date_time(datetime, 
                                  orders = c('%m/%d/%Y %H:%M',
                                             '%Y-%m-%d %H:%M'))) %>%
  select(-datetime)

## Rows: 49630 Columns: 19
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr  (1): datetime
## dbl (18): SnowD_m, Rn_Wm2, u_ms, VWC_5, VWC_20, VWC_50, Ts_5, Ts_20, Ts_50, ...
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.

# read in tipping bucket data
rain <- read_csv(here('bighorn_rain_composite_new.csv')) %>%
  mutate(dt_use = parse_date_time(datetime, 
                                  orders = c('%m/%d/%Y %H:%M',
                                             '%Y-%m-%d %H:%M'))) %>%
  select(-datetime)

## Rows: 108597 Columns: 2
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): datetime
## dbl (1): tips
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## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.

# read in stage data
stage <- read_csv(here('bighorn_stage_composite_new.csv')) %>%
  mutate(dt_use = parse_date_time(DateTime, 
                                  orders = c('%m/%d/%Y %H:%M',
                                             '%m/%d/%y %I:%M:%S %p'))) %>%
  select(-c(DateTime, T_C)) %>%
  rename(Pw_kPa = P_kPa)

## Rows: 201631 Columns: 3
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): DateTime
## dbl (2): P_kPa, T_C
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## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.

# read in baro data
baro <- read_csv(here('bighorn_baro_composite_new.csv')) %>%
  mutate(dt_use = parse_date_time(DateTime, 
                                  orders = c('%m/%d/%Y %H:%M',
                                             '%m/%d/%y %I:%M:%S %p'))) %>%
  select(-c(DateTime, T_C)) %>%
  rename(Pa_kPa = P_kPa)

## Rows: 203211 Columns: 3
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): DateTime
## dbl (2): P_kPa, T_C
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## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.

#join stage and baro DFs to get stage height
stage <- left_join(stage, baro, by="dt_use")

# get stage in kPa, convert to stage in cm
stage <- stage %>%
  mutate(stage_kPa = Pw_kPa -Pa_kPa,
         stage_cm = stage_kPa * 10.197162129779)

Plotting a basic time series of each variable

snowdepth <- ggplot(met, aes(x= dt_use, y = SnowD_m)) + 
  geom_point() +
  ggtitle('snow depth (m)')

ggplotly(snowdepth)

netrad_old <- ggplot(met, aes(x= dt_use, y = Rn_Wm2)) + 
  geom_point() +
  ggtitle('net rad_old (Wm2)')

ggplotly(netrad_old)

windspeed <- ggplot(met, aes(x= dt_use, y = u_ms)) + 
  geom_point() +
  ggtitle('wind speed (m/s)')

ggplotly(windspeed)

sm <- ggplot(met, aes(x=dt_use)) +
  geom_line(aes(y=VWC_5, color='5cm')) +
  geom_line(aes(y=VWC_20, color='20cm')) +
  geom_line(aes(y=VWC_50, color='50cm')) +
  ggtitle('soil moisture (%)')

ggplotly(sm)

soiltemp <- ggplot(met, aes(x=dt_use)) +
  geom_line(aes(y=Ts_5, color='5cm')) +
  geom_line(aes(y=Ts_20, color='20cm')) +
  geom_line(aes(y=Ts_50, color='50cm')) +
  ylim(-50,50) + #add this y lim since Ts_20 went back and had high readings 
  ggtitle('soil temp (deg C)')

ggplotly(soiltemp)